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Abstract and Applied Analysis
Volume 2012 (2012), Article ID 306583, 14 pages
http://dx.doi.org/10.1155/2012/306583
Research Article

New LMI-Based Conditions on Neural Networks of Neutral Type with Discrete Interval Delays and General Activation Functions

1School of Mechanical and Electronic Engineering, East China Institute of Technology, Nanchang 330013, China
2Science and Technology on UAV Laboratory, Northwestern Polytechnical University, Xi'an 710072, China

Received 23 August 2012; Accepted 6 October 2012

Academic Editor: Sabri Arik

Copyright © 2012 Guoquan Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. He and L. Wang, “Chaotic neural networks and their application to optimization problems,” Control Theory & Applications, vol. 17, pp. 847–852, 2000. View at Zentralblatt MATH
  2. V. Miljković, S. Milosevic, R. Sknepnek, and I. Zivic, “Pattern recognition in damaged neural networks,” Physica A, vol. 295, no. 3-4, pp. 526–536, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  3. H. Kirschner and R. Hillebrand, “Neural networks for HREM image analysis,” Information sciences, vol. 129, no. 1–4, pp. 31–44, 2000. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  4. A. Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal Processing, John Wiley & Sons, Chichester, UK, 1993.
  5. M. Itoh and L. O. Chua, “Star cellular neural networks for associative and dynamic memories,” International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, vol. 14, no. 5, pp. 1725–1772, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  6. G. Labonte and M. Quintin, “Network parallel computing for SOM neural networks,” High Performance Computing Systems and Applications, vol. 541, pp. 575–586, 2000.
  7. C. X. Huang and J. D. Cao, “Almost sure exponential stability of stochastic cellular neural networks with unbounded distributed delays,” Neurocomputing, vol. 72, no. 13–15, pp. 3352–3356, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. H. J. Xiang and J. D. Cao, “Almost periodic solution of Cohen-Grossberg neural networks with bounded and unbounded delays,” Nonlinear Analysis: Real World Applications, vol. 10, no. 4, pp. 2407–2419, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  9. S. Arik, “Global robust stability of delayed neural networks,” IEEE Transactions on Circuits and Systems I, vol. 50, no. 1, pp. 156–160, 2003. View at Publisher · View at Google Scholar
  10. X. F. Liao and C. D. Li, “An LMI approach to asymptotical stability of multi-delayed neural networks,” Physica D, vol. 200, no. 1-2, pp. 139–155, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  11. H. D. Qi, “New sufficient conditions for global robust stability of delayed neural networks,” IEEE Transactions on Circuits and Systems I, vol. 54, no. 5, pp. 1131–1141, 2007. View at Publisher · View at Google Scholar
  12. V. Singh, “Improved global robust stability criterion for delayed neural networks,” Chaos, Solitons and Fractals, vol. 31, no. 1, pp. 224–229, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  13. X. G. Liu, R. R. Martin, M. Wu, and M. L. Tang, “Global exponential stability of bidirectional associative memory neural networks with time delays,” IEEE Transactions on Neural Networks, vol. 19, no. 3, pp. 397–407, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Y. Xu, J. Lam, D. W. C. Ho, and Y. Zou, “Delay-dependent exponential stability for a class of neural networks with time delays,” Journal of Computational and Applied Mathematics, vol. 183, no. 1, pp. 16–28, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  15. Y. J. Zhang, S. Y. Xu, Y. M. Chu, and J. J. Lu, “Robust global synchronization of complex networks with neutral-type delayed nodes,” Applied Mathematics and Computation, vol. 216, no. 3, pp. 768–778, 2010. View at Publisher · View at Google Scholar
  16. R. Samli and S. Arik, “New results for global stability of a class of neutral-type neural systems with time delays,” Applied Mathematics and Computation, vol. 210, no. 2, pp. 564–570, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  17. Z. Orman, “New sufficient conditions for global stability of neutral-type neural networks with time delays,” Neurocomputing, vol. 97, pp. 141–148, 2012. View at Publisher · View at Google Scholar
  18. J. H. Park, O. M. Kwon, and S. M. Lee, “State estimation for neural networks of neutral-type with interval time-varying delays,” Applied Mathematics and Computation, vol. 203, no. 1, pp. 217–223, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  19. J. H. Park and O. M. Kwon, “Further results on state estimation for neural networks of neutral-type with time-varying delay,” Applied Mathematics and Computation, vol. 208, no. 1, pp. 69–75, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  20. J. H. Park and O. M. Kwon, “Global stability for neural networks of neutral-type with interval time-varying delays,” Chaos, Solitons and Fractals, vol. 41, no. 3, pp. 1174–1181, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  21. R. Rakkiyappan and P. Balasubramaniam, “New global exponential stability results for neutral type neural networks with distributed time delays,” Neurocomputing, vol. 71, no. 4–6, pp. 1039–1045, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. J. E. Feng, S. Y. Xu, and Y. Zou, “Delay-dependent stability of neutral type neural networks with distributed delays,” Neurocomputing, vol. 72, no. 10–12, pp. 2576–2580, 2009. View at Publisher · View at Google Scholar · View at Scopus